BIOSTATISITCS AND RESEARCH METHODOLOGY BP801T

UNIT -01

Introduction: Statistics, Biostatistics, Frequency distribution. Measures of central tendency: Mean, Median, Mode- Pharmaceutical examples. Measures of dispersion: Dispersion, Range, standard deviation, Pharmaceutical problems. Correlation: Definition, Karl Pearson’s coefficient of correlation, multiple correlationPharmaceuticals examples.

Regression: Curve fitting by the method of least squares, fitting the lines y= a + bx and x = a + by, Multiple regression, standard error of regression– Pharmaceutical examples. Probability: Definition of probability, Binomial distribution, Normal distribution, Poisson’s distribution, properties– problems. Sample, Population, large sample, small sample, Null hypothesis, alternative hypothesis, sampling, essence of sampling, types of sampling, Error-I type, Error-II type, Standard error of mean (SEM) – Pharmaceutical examples. Parametric test: t-test (Sample, Pooled or Unpaired and Paired), ANOVA, (One way and Two way), Least Significance difference.

Non-Parametric tests: Wilcoxon Rank Sum Test, Mann-Whitney U test, Kruskal-Wallis test, Friedman Test. Introduction to Research: Need for research, Need for design of Experiments, Experiential Design Technique, Plagiarism. Graphs: Histogram, Pie Chart, Cubic Graph, response surface plot, Counter Plot graph Designing the methodology: Sample size determination and Power of a study, Report writing and presentation of data, Protocol, Cohorts studies, Observational studies, Experimental studies, Designing clinical trial, various phases.

Blocking and confounding system for Two-level factorials. Regression modeling: Hypothesis testing in Simple and Multiple regression models Introduction to Practical components of Industrial and Clinical Trials Problems: Statistical Analysis Using Excel, SPSS, MINITAB®, Design of experiment, R- Online Statistical Software’s to Industrial and Clinical trial approach.

Design and Analysis of experiments: Factorial Design: Definition, 22 , 23 design. Advantages of factorial design. Response Surface methodology: Central composite design, Historical design, Optimization Techniques.